Storage of shard data

ABSTRACT

A method for storing shard data and data formats for storing shard data are proposed. Shard data entries are generated and stored, wherein each shard data entry comprises a definition of one or more semantic objects covered by the shard data entry. Shard metadata is generated and stored, wherein the metadata comprises references to the shard data entries and, for each of the shard data entries, data representative of a bounding box indicative of an area in a geographical area that is covered by the shard data entry.

TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure relates generally to autonomous vehicles. Morespecifically, the present disclosure relates to methods for storingshard data for autonomous vehicles.

BACKGROUND

Autonomous vehicles, also known as self-driving cars, driverlessvehicles, and robotic vehicles, are vehicles that use multiple sensorsto sense the environment and move without human input. Automationtechnology in the autonomous vehicles enables the vehicles to drive onroadways and to accurately and quickly perceive the vehicle'senvironment, including obstacles, signs, and traffic lights. Autonomoustechnology typically makes use of map data that can include geographicalinformation and semantic objects (such as parking spots, laneboundaries, intersections, crosswalks, stop signs, traffic lights) forfacilitating driving safety. The vehicles can be used to pick uppassengers and drive the passengers to selected destinations. Thevehicles can also be used to pick up packages and/or other goods anddeliver the packages and/or goods to selected destinations.

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present disclosure andfeatures and advantages thereof, reference is made to the followingdescription, taken in conjunction with the accompanying figures, whereinlike reference numerals represent like parts, in which:

FIG. 1 is a diagram illustrating an autonomous vehicle, according tosome embodiments of the disclosure;

FIG. 2 is a diagram illustrating a fleet of autonomous vehicles incommunication with a central computer, according to some embodiments ofthe disclosure;

FIG. 3 is a diagram illustrating a software development environment,according to some embodiments of the disclosure;

FIG. 4 is a diagram illustrating a system architecture for generatingmap data in the form of shard data, according to some embodiments of thedisclosure;

FIG. 5 is a diagram illustrating clients each using a dedicated copy ofshard data, according to some embodiments of the disclosure;

FIG. 6 is a diagram illustrating clients sharing shard data, accordingto some embodiments of the disclosure;

FIG. 7 is a geographical area that has been split in sections, accordingto some embodiments of the disclosure;

FIG. 8 is a flow diagram of a method for splitting a geographical areainto sections, according to some embodiments of the disclosure;

FIG. 9 is a flow diagram of a method for generating shard data,according to some embodiments of the disclosure;

FIG. 10 is a flow diagram showing more details of a part of a method forgenerating shard data, according to some embodiments of the disclosure;

FIG. 11 is a flow diagram of a method for exporting shards from a sharddatabase, according to some embodiments of the disclosure;

FIG. 12 is a flow diagram of a method for uploading shards to a storage,according to some embodiments of the disclosure;

FIG. 13 is a flow diagram of a method for requesting shards by a client,according to some embodiments of the disclosure;

FIG. 14 is a flow diagram of a method for requesting map data by aclient, according to some embodiments of the disclosure;

FIG. 15 is a flow diagram of a method for dynamically loading shards fora client, according to some embodiments of the disclosure; and

FIG. 16 shows an example embodiment of a system for implementing certainaspects of the present technology.

DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE DISCLOSURE

Overview

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for allof the desirable attributes disclosed herein. Details of one or moreimplementations of the subject matter described herein are set forthbelow and the accompanying drawings.

It will be recognized that maps are a key component in autonomousvehicle technology. In particular, the highly specialized needs ofautonomous vehicles require highly specialized, and high definition,maps that represent the world at a level of resolution one to two ordersof magnitude greater than offered by regular web map services that arewidely used for turn-by-turn navigation. Such high resolution isnecessary because autonomous vehicles are routinely required to executecomplex maneuvers, such as safely navigating and accommodating bikelanes and pedestrian paths along roadways, for example.

An autonomous vehicle mapping system, or database, may include severallayers, or types, of maps or mapping information. For example, a mappingdatabase may include a standard mapping layer, a geometric mappinglayer, a semantic mapping layer, a map priors layer, and/or a real-timeinformation layer. The standard mapping layer includes mappinginformation at a level similar/identical to that available from theaforementioned web mapping services. The geometric mapping layerincludes three-dimensional (3D) mapping information for defining adrivable portion of a mapped area. The semantic mapping layer builds onthe geometric mapping information by adding semantic objects, includingtwo-dimensional (2D) and 3D traffic objects (such as parking spots, laneboundaries, intersections, crosswalks, stop signs, traffic lights) forfacilitating driving safety. Semantic objects may include associatedmetadata, such as speed limits and turn restrictions for lanes, forexample. The map priors layer includes derived information about dynamicelements as well as human driving behavior. The optional real-timeinformation layer is typically read/write enabled and is a portion ofthe mapping database designed to be updated while the autonomous vehicleis providing an autonomous vehicle service and (as its name implies)contains real-time data, such as traffic congestion, newly discoveredconstruction zones, observed speeds, etc. The real-time information isdesigned to support gathering and sharing of real-time globalinformation among a fleet of autonomous vehicles.

From the information in the autonomous vehicle mapping system, map datamay be generated that combines one or more of the layers into a semanticmap. The present disclosure relates to the generation and use of mapdata.

Map data typically includes data required by an autonomous vehicle tomake driving decisions and is traditionally generated as a data file ordata container that includes a semantic map of a geographical area. Suchdata file or data container may be referred to as monolithic map data.

The geographical area traditionally covered by monolithic map data is,e.g., a city or a country. Map data may be generated at a centralcomputer system, from where it may be uploaded or otherwise installedonto a data storage of the autonomous vehicles, typically located in theautonomous vehicle.

Additionally, or alternatively, map data may be used for softwaredevelopment, where, e.g., autonomous vehicles or parts thereof may besimulated to develop new features or improve existing features of theautonomous vehicles. The simulation environment can make use of map datasimilar to an autonomous vehicle using such map data. To allow teams ofdevelopers to work on the software developments, the simulationenvironment may be distributed to the different teams, who may work ondifferent aspects of the software. The map data is typically copied toeach of the teams to allow dedicated access to the map data to thedevelopment teams.

Depending on the size of the geographical area covered by the monolithicmap data, the data file or data container can be very large. For alarger city, a data file can be hundreds of megabytes or even gigabytesin size. To cover a country, a monolithic map data file can be gigabytesin size for a smaller country or terabytes in size for a larger country.Uploading monolithic map data of these sizes to autonomous vehiclesand/or software development teams requires large network capacity andlarge data plans. Furthermore, when processing or using the monolithicmap data at the autonomous vehicle or simulation environment, processingrequirements (e.g., CPU requirements) and/or working memory requirement(e.g., RAM capacity) may be high, especially when the semantic map isloaded and unpacked into memory in full from the monolithic map data.

The present disclosure aims to lower or minimize the size of the mapdata, thereby lowering network capacity, processing and/or memoryrequirements. In this disclosure, autonomous vehicles and simulationdevices are also referred to as “clients”, as in client devices to theautonomous vehicle mapping system.

According to some embodiments of the present disclosure, semantic mapsharding reduces or minimizes the size of the map data loaded and/orprocessed by a client. With semantic map sharding, a segmentationalgorithm generates semantic map shards (also referred to as “shards” or“shard data”), which may be used as an alternative to monolithic mapdata.

In some embodiments, shards may be provided by the mapping system aswell as monolithic map data, e.g., in case different geographical areas(e.g., different cities or different countries) are covered by eithermonolithic map data or shards-based data, or, e.g., for backwardcompatibility where some autonomous vehicles support monolithic map dataand other autonomous vehicles support shard data.

The concept of semantic map sharding introduces various changes to thesystems, methods and data formats used in traditional monolithic mapdata generation and usage.

According to some aspects of the present disclosure, systems and methodsare proposed for generating shard data based on semantic map sharding.Shard data may be generated by a map sharder that is configured togenerate the shard data based on semantic objects in a geographical areaand a definition of sections. The sections may cover parts of thegeographical area. The map sharder may search, for each of the sections,for semantic objects that are located at least partly in a section andstores found objects in a shard data entry of the shard data.

According to some aspects of the present disclosure, systems and methodsare presented to generate shard data from a geographical area that hasbeen split into sections. The system and method may use datarepresentative of a geographical area that is split into sections anddata representative of semantic objects located in the geographicalarea. For each of the sections, semantic objects may be searched thatare located at least partly in a section. A shard data entry of theshard data may be generated. The shard data entry may include one ormore semantic objects found by the searching.

According to some aspects of the present disclosure, a rules basedsemantic map sharding is proposed. A method of handling semantic objectsfor semantic map sharding may make used of rules to define whichsemantic objects are to be included in a shard. Hereto, semantic objectsmay be defined in a geographical area to be contained in shard data. Forthe semantic objects, a set of rules may be defined including adefinition of which semantic objects are to be combined into a sharddata entry of the shard data and which semantic objects are to be storedin separate shard data entries.

According to some aspects of the present disclosure, a method forstoring shard data and data formats for storing shard data are proposed.Shard data entries may be generated and stored, wherein each shard dataentry may include a definition of one or more semantic objects coveredby the shard data entry. Shard metadata may be generated and stored,wherein the metadata may include references to the shard data entriesand, for each of the shard data entries, data representative of abounding box indicative of an area in a geographical area that iscovered by the shard data entry.

Semantic Map Shards

With semantic map sharding, a semantic map, i.e., a map that combinesone or more layers (e.g., standard mapping layer, geometric mappinglayer, semantic mapping layer, maps priors layer, and/or real-timeinformation layer) is split into logical chunks called shards. Theshards together may be referred to as shard data or the plural form“shards”. A single shard may be referred to as a shard data entry or thesingular form “shard”. Shards are different from tiles, which typicallysplit a raster map into equally sized tiles. Due to the fact that asemantic map is not a raster, but rather a vector/object representationof the world, the semantic map cannot be not suitably split into equallysized tiles.

A semantic map shard is a data object that contains semantic objectsand/or links between semantic objects in a specific area. A bounding boxindicative of the area covered by a shard may be calculated based on theobjects in that shard and stored with the shard. The bounding boxes ofshards may be used to determine which shards cover a specific location.The bounding box includes, e.g., data indicative of four corners of anarea covered by the shard. A bounding box is not limited to having fourcorners. More generally, a bounding box may have any polygonal shapewith three or more vertices, in which case the bounding box includes,e.g., data indicative of each of the vertices of the polygonal shapedarea covered by the shard.

As different objects typically cover different areas, the bounding boxwill typically be different for different shards. For example, car lanesfrom a first intersection to a second intersection may be stored in afirst shard. Car lanes from a third intersection to a fourthintersection may be stored in a second shard. As the length of the roadsin the first and second shards may be different in length and/ordifferently shaped, the bounding boxes of the first and second shardsare likely to be different in size. In another example, a rail line maybe located next to the car lanes of the first shard. The rail line maybe stored in a third shard, which covers all rail lines in a city andtherefore has a larger bounding box.

Shards can include any set of semantic objects. Preferably, a semanticobject is contained in one shard only to avoid data integrity orprocessing problems, e.g., to avoid that a single semantic object isloaded multiple times from multiple shards but interpreted as differentobjects. A shard need not include all objects in a specific area.Instead, one type of objects or closely related objects may be stored inthe same shard and other objects in the same area may be stored inanother shard.

Example Autonomous Vehicle Configured for Using Semantic Map Shards

FIG. 1 is a diagram 100 illustrating an autonomous vehicle 110,according to some embodiments of the disclosure. The autonomous vehicle110 includes a sensor suite 102 and an onboard computer 104. In variousimplementations, the autonomous vehicle 110 uses sensor information fromthe sensor suite 102 to determine its location, to navigate traffic, tosense and avoid obstacles, and to sense its surroundings. According tovarious implementations, the autonomous vehicle 110 may be part of afleet of vehicles for picking up passengers and/or packages and drivingto selected destinations. The autonomous vehicle 110 may be configuredfor ride management by an event host.

The sensor suite 102 includes localization and driving sensors. Forexample, the sensor suite may include one or more of photodetectors,cameras, RADAR, SONAR, LIDAR, GPS, inertial measurement units (IMUS),accelerometers, microphones, strain gauges, pressure monitors,barometers, thermometers, altimeters, wheel speed sensors, and acomputer vision system. The sensor suite 102 continuously monitors theautonomous vehicle's environment and, in some examples, sensor suite 102data is used to detect selected events. In particular, data from thesensor suite can be used to update a map with information used todevelop layers with waypoints identifying selected events, the locationsof the encountered events, and the frequency with which the events areencountered at the identified location. In this way, sensor suite 102data from many autonomous vehicles can continually provide feedback tothe mapping system and the high fidelity map can be updated as more andmore information is gathered.

In various examples, the sensor suite 102 includes cameras implementedusing high-resolution imagers with fixed mounting and field of view. Infurther examples, the sensor suite 102 includes LIDARs implemented usingscanning LIDARs. Scanning LIDARs have a dynamically configurable fieldof view that provides a point-cloud of the region intended to scan. Instill further examples, the sensor suite 102 includes RADARs implementedusing scanning RADARs with dynamically configurable field of view.

The autonomous vehicle 110 includes an onboard computer 104, whichfunctions to control the autonomous vehicle 110. The onboard computer104 processes sensed data from the sensor suite 102 and/or othersensors, in order to determine a state of the autonomous vehicle 110.Based upon the vehicle state and programmed instructions, the onboardcomputer 104 controls and/or modifies driving behavior of the autonomousvehicle 110.

The onboard computer 104 functions to control the operations andfunctionality of the autonomous vehicle 110 and processes sensed datafrom the sensor suite 102 and/or other sensors in order to determinestates of the autonomous vehicle. In some implementations, the onboardcomputer 104 is a general-purpose computer adapted for I/O communicationwith vehicle control systems and sensor systems. In someimplementations, the onboard computer 104 is any suitable computingdevice. In some implementations, the onboard computer 104 is connectedto the Internet via a wireless connection (e.g., via a cellular dataconnection). In some examples, the onboard computer 104 is coupled toany number of wireless or wired communication systems. In some examples,the onboard computer 104 is coupled to one or more communication systemsvia a mesh network of devices, such as a mesh network formed byautonomous vehicles.

The autonomous vehicle 110 is preferably a fully autonomous automobile,but may additionally or alternatively be any semi-autonomous or fullyautonomous vehicle. In various examples, the autonomous vehicle 110 is aboat, an unmanned aerial vehicle, a driverless car, a golf cart, atruck, a van, a recreational vehicle, a train, a tram, a three-wheeledvehicle, or a scooter. Additionally, or alternatively, the autonomousvehicles may be vehicles that switch between a semi-autonomous state anda fully autonomous state and thus, some autonomous vehicles may haveattributes of both a semi-autonomous vehicle and a fully autonomousvehicle depending on the state of the vehicle.

In various implementations, the autonomous vehicle 110 includes athrottle interface that controls an engine throttle, motor speed (e.g.,rotational speed of electric motor), or any other movement-enablingmechanism. In various implementations, the autonomous vehicle 110includes a brake interface that controls brakes of the autonomousvehicle 110 and controls any other movement-retarding mechanism of theautonomous vehicle 110. In various implementations, the autonomousvehicle 110 includes a steering interface that controls steering of theautonomous vehicle 110. In one example, the steering interface changesthe angle of wheels of the autonomous vehicle. The autonomous vehicle110 may additionally or alternatively include interfaces for control ofany other vehicle functions, for example, windshield wipers, headlights,turn indicators, air conditioning, etc.

The autonomous vehicle 110 may include a map storage 106 for storing mapdata. The autonomous vehicle 110 may use the map data in various drivingdecisions, e.g., in finding optimal routes, in support of detectingobjects along a route such as traffic lights, or for predicting behaviorof other road users and planning autonomous vehicle behavior.

According to some embodiments of the present disclosure, semantic mapsharding may be used to reduce or minimize the size of the map datadownloaded or stored in the autonomous vehicle 110, e.g., in map storage106. The smaller data size of shard data compared to monolithic map datamay reduce network capacity requirements for downloading the map data tothe autonomous vehicle 110. Additionally, or alternatively, shard datamay reduce processor load and/or memory usage of the onboard computer104 when processing map data stored in map storage 106 of the autonomousvehicle 110. With semantic map sharding, shard data may be downloaded orotherwise installed in the autonomous vehicle 110. Shards may be used bythe autonomous vehicle 110 as an alternative to monolithic map data ormay be used next to monolithic map data, e.g., in case different areasare covered by either monolithic map data or by shard data.

Example of Autonomous Vehicle Fleet Using Semantic Map Shards

FIG. 2 is a diagram 200 illustrating a fleet of autonomous vehicles 210a, 210 b, 210 c in communication with a central computer 202, accordingto some embodiments of the disclosure. As shown in FIG. 2, the vehicles210 a-210 c communicate wirelessly with a cloud 204 and a centralcomputer 202. The central computer 202 may include a routing coordinatorand a database of information from the vehicles 210 a-210 c in thefleet. Each vehicle 210 a-210 c can include respective onboard computer220 a-220 c and map storage 222 a-222 c, which can be similar to theonboard computer 104 and map storage 106 of FIG. 1. Specifically,semantic map sharding may be used to reduce or minimize the size of themap data downloaded or stored in the vehicles 210 a-210 c, e.g., in mapstorage 222 a-222 c. Moreover, semantic map sharding may reduceprocessor load and/or memory usage of the onboard computer 220 a-220 cwhen processing the map data.

Autonomous vehicle fleet routing refers to the routing of multiplevehicles in a fleet. In some implementations, autonomous vehiclescommunicate directly with each other.

When a ride request is received from a passenger, the routingcoordinator may select an autonomous vehicle 210 a-210 c to fulfill theride request, and generates a route for the autonomous vehicle 210 a-210c. As described herein, in some examples, the routing coordinatorselects more than one autonomous vehicle 210 a-210 c to fulfill the riderequest. The generated route includes a route from the autonomousvehicle's present location to the pick-up location, and a route from thepick-up location to the final destination. In some examples, thegenerated route includes a route from the pick-up location to a selectedwaypoint, and a route from the selected waypoint to the finaldestination. In some examples, a first autonomous vehicle 210 a drivesthe route to the waypoint and a second autonomous vehicle 210 b drivesthe route from the waypoint to the final destination. In variousexamples, the route includes multiple waypoints and multiple autonomousvehicles. In some implementations, the central computer 202 communicateswith a second fleet of autonomous vehicles, and a vehicle from thesecond fleet of autonomous vehicles drives the route from the waypointto the final destination.

Each vehicle 210 a-210 c in the fleet of vehicles may communicate with arouting coordinator. Information gathered by various autonomous vehicles210 a-210 c in the fleet can be saved and used to generate informationfor future routing determinations. For example, sensor data can be usedto generate route determination parameters. In general, the informationcollected from the vehicles in the fleet can be used for routegeneration or to modify existing routes. In some examples, the routingcoordinator collects and processes position data from multipleautonomous vehicles in real-time to avoid traffic and generate afastest-time route for each autonomous vehicle. In some implementations,the routing coordinator uses collected position data to generate a bestroute for an autonomous vehicle in view of one or more travellingpreferences and/or routing goals.

The routing coordinator uses map data to select an autonomous vehiclefrom the fleet to fulfill a ride request. In some implementations, therouting coordinator sends the selected autonomous vehicle the riderequest details, including pick-up location and destination location,and an onboard computer (e.g., onboard computer 220 a, 220 b, or 220 c)on the selected autonomous vehicle generates a route and navigates tothe destination. In some examples, the routing coordinator also sendsthe selected vehicle one or more stops, including a charging stationstop, for the autonomous vehicle to recharge. In some examples, therouting coordinator sends a first vehicle the pick-up location and awaypoint location, and the routing coordinator sends a second vehiclethe waypoint location and the destination location, such that thepassenger switches vehicles mid-ride. In some implementations, therouting coordinator in the central computer 202 generates a route foreach selected autonomous vehicle 210 a-210 c, and the routingcoordinator determines a route for the autonomous vehicle 210 a-210 c totravel from the autonomous vehicle's current location to a first stop.

According to some embodiments of the present disclosure, semantic mapsharding may be used to reduce or minimize the size of the map datastored in map storage 230. The smaller data size of shard data comparedto monolithic map data may reduce network capacity requirements fordownloading the map data from the map storage 230. Alternatively, oradditionally, shard data may reduce processor load and memory usage ofthe central computer 202 when processing map data stored in map storage230. Shards may be stored in map storage 230 as an alternative tomonolithic map data or may be used next to monolithic map data, e.g., incase different areas are covered by either monolithic map data or byshard data.

Example Development Environment Configured for Using Semantic Map Shards

Various development platforms and software work flows exist fordeveloping software. A software workflow that is particularly suitablefor the development of software for autonomous vehicles is a continuousintegration (CI) workflow. CI is the practice of merging software codechanges in small code changes frequently, rather than merging in a largechange at the end of a development cycle. An example of a CI developmentenvironment is the Travis™ CI, which offers a CI service to build andtest software projects hosted on e.g., GitHub™ or Bitbucket™.

FIG. 3 is a diagram 300 illustrating a CI software developmentenvironment, according to some embodiments of the disclosure. In theexample of FIG. 3, software code development is divided over threeteams, which may be located at different locations. Each team may use acomputer environment 320 a-320 c for coding and testing portions ofsoftware for autonomous vehicles. To allow the computer environments 320a-320 c to test software changes, access to the map data may berequired. Hereto, each computer environment 320 a-320 c can includerespective map storage 322 a-322 c for storing map data for use by thecomputer environments 320 a-320 c. Specifically, semantic map shardingmay be used to reduce or minimize the size of the map data downloaded orstored in the computer environments 320 a-320 c, e.g., in map storage322 a-322 c. Moreover, semantic map sharding may reduce processor loadand/or memory usage of the computer environments 320 a-320 c whenprocessing the map data.

Software code changes may be committed (depicted by arrows 304) from thecomputer environments 320 a-320 c to a source control server 330, whichcollects the software code changes and periodically, e.g., once a day,uploads (depicted by arrow 306) the changed software code to a CI server310. Alternatively, the CI server 310 may fetch the software codechanges from the source control server 330 periodically.

The modified software code, including software code changes fromcomputer environments 320 a, 320 b and/or 320 c, may be built and testedat the CI server 310. The CI server 310 may operate as a simulationenvironment mimicking an autonomous vehicle to test the software codechanges. Test results, such as failure indications or performancemetrics, may be logged by the CI server 310. The process of building,testing and logging test results are depicted by the arrows 308 in FIG.3. To allow the CI server 310 to test the software code, access to themap data may be required. Hereto, the CI server 310 can include a mapstorage 312 for storing the map data for the CI server 310.Specifically, semantic map sharding may be used to reduce or minimizethe size of the map data downloaded or stored in the CI server 310,e.g., in map storage 312. Moreover, semantic map sharding may reduceprocessor load and/or memory usage of the CI server 310 when processingthe map data.

The test results, or parts thereof, may be reported (depicted by arrows302) from the CI server 310 to the computer environments 320 a-320 c toallow further code improvements to be made by the respective developmentteams.

The map storage 322 a-322 c and/or 312 may be a local storage at theeach of the computer environments 320 a-320 c or CI server 310.Alternatively, the map storage 322 a-322 c may be a cloud storage foruse by the computer environment 320 a-320 c and/or CI server 310. Acloud storage may be dedicated to one of the computer environments 320a-320 c and/or CI server 310, or shared by two or more of the computerenvironments 320 a-320 c and/or CI server 310.

Not depicted in FIG. 3, the process of software developing may make useof test data originating from autonomous vehicles driving around in ageographical area, e.g., in a city. This test data, or parts thereof,may be provided to one or more of the computer environments 320 a-320 cand/or the CI server 310, e.g., to test existing features of theautonomous vehicle against the test data or develop new features.

According to some embodiments of the present disclosure, semantic mapsharding may be used to reduce or minimize the size of the map datadownloaded or stored in a computer environment and/or a CI server, e.g.,in map storage 322 a-322 c of computer environments 320 a-320 c and/ormap storage 312 of CI server 310. The smaller data size of shard datacompared to monolithic map data may reduce network capacity requirementsfor downloading the map data to the computer environments 320 a-320 cand to CI server 310. Additionally, or alternatively, shard data mayreduce processor load and/or memory usage of the computer environments320 a-320 c and the CI server 310 when processing map data.

Example of a System Architecture for Generating Semantic Map Shards

Semantic map shards, such as the shards stored in map storage 106, 220a-220 c, 230, 312 or 322 a-322 c are generated according to a novelprocess. An example of a system architecture for generating semantic mapshards based on this novel process is shown in FIG. 4.

FIG. 4 shows a system architecture 400 for generating semantic mapshards, according to some embodiments of the disclosure. The systemarchitecture 400 may be a part of central computer 202, part of cloud204, and/or implemented separate from central computer 202 and cloud204. It is possible that parts of the system architecture 400 areimplemented in different computers and/or cloud computing environments.

The system architecture 400 may include a database storage 410 (depicted‘DB Storage 410’ in FIG. 4), or any other suitable data storage, fromwhich data may be read and to which data may be written by a map sharder401 (depicted ‘Map Shrdr 401’ in FIG. 4). The map sharder 401 may beimplemented as a computer system, wherein a processor is configured toexecute computer code to perform a map sharder function. The databasestorage 410 and map sharder 401 may be a part of the same computersystem. Alternatively, the database storage 410 may be implemented as aseparate database storage that is communicatively connected to the mapsharder 401, e.g., via a local area network. It will be understood thatany other computer configuration may be used for the database storage410 and map sharder 401, including but not limited to database storage410 being accessible by the map sharder 401 via a wide area network orinvolving cloud computing services.

The database storage 410 may store data that can be input to the mapsharder 401 to enable the generation of semantic map shards by the mapsharder 401. For example, semantic objects may be stored in any suitabledatabase format in a semantic objects database 411 (depicted ‘Sem Obj411’ in FIG. 4). The semantic objects typically include objects presentin the semantic mapping layer, such as two-dimensional (2D) and 3Dtraffic objects (such as parking spots, lane boundaries, intersections,crosswalks, stop signs, traffic lights) for facilitating driving safety.Metadata associated with the semantic objects may be included in thesemantic objects database 411, such as speed limits and turnrestrictions for lanes.

The database storage 410 may further store shard guiding sections in anysuitable database format in a shard guiding sections database 412(depicted ‘Shrd Gd 412’ in FIG. 4). Shard guiding sections definedifferent parts of a geographical area and may be used by the mapsharder 401 as a guide to find semantic objects in the semantic objectsdatabase 411 that are covered by a shard guiding section.

The map sharder 401 may generate semantic map shards based on the inputdata, the input data typically including semantic objects from asemantic objects database 411 and guiding sections from a shard guidingsections database 412. The generated semantic map shards may be storedin the database storage 410 in any suitable database format. In theexample of FIG. 4, the semantic map shards are stored in two databases.A shard metadata database 413 (depicted ‘Shrd Mta 413’ in FIG. 4) maystore metadata related to shards. A shard-to-object relations database414 (depicted ‘Shrd-Obj 414’ in FIG. 4) may store shard to objectrelations data, thereby defining which semantic objects are part of ashard.

It will be understood that the data stored in the database storage 410may be stored in various manners. For example, one or more of thedatabases 411-414 may be implemented as separate databases or may beimplemented as a single database using different tables. Any othersuitable storage method may be used to store the databases 411-414 inthe database storage 410.

The system architecture 400 may further include a database exporter 420(depicted ‘Exporter 420’ in FIG. 4). The database exporter 420 may beimplemented as a computer system, wherein a processor is configured toexecute computer code to perform an export function. The databasestorage 410 and database exporter 420 may be a part of the same computersystem. Alternatively, the database storage 410 may be implemented as aseparate database storage that is communicatively connected to thedatabase exporter 420, e.g., via a local area network. It will beunderstood that any other computer configuration may be used for thedatabase storage 410 and database exporter 420, including but notlimited to database storage 410 being accessible by the databaseexporter 420 via a wide area network or involving cloud computingservices.

The database exporter 420 may be configured to export shard data fromthe database storage 410 to a file storage system 430 (depicted ‘FileStorage 430’ in FIG. 4). Hereto, the database exporter 420 may readshard metadata from the shard metadata database 413 and shard objectsinformation from the shard-to-object relations database 414. Theexported shard data may be stored in shard files 432 (depicted ‘ShrdFile432’ in FIG. 4) and shard metadata 433 (depicted ‘Shrd Mta 433’ in FIG.4). The shard files 432 are preferably stored such that one fileincludes data for one shard. The shard metadata 433 may be stored in asingle file. The file storage system 430 enables efficient distributionof the shards to clients.

The database exporter 420 may be configured to generate monolithic mapdata 431 next to the shard files 432. The monolithic map data 431(depicted ‘Monolith. 431’ in FIG. 4) may also be stored in the filestorage system 430.

The system architecture 400 may further include an uploader 440(depicted ‘Uploader 440’ in FIG. 4). The uploader 440 may be implementedas a computer system, wherein a processor is configured to executecomputer code to perform a shard upload function. The file storagesystem 430 and uploader 440 may be a part of the same computer system.Alternatively, the file storage system 430 may be implemented as aseparate file storage that is communicatively connected to the uploader440, e.g., via a local area network. It will be understood that anyother computer configuration may be used for the file storage system 430and uploader 440, including but not limited to the file storage system430 being accessible by the uploader 440 via a wide area network orinvolving cloud computing services.

The uploader 440 may include a shard uploader 441 (depicted ‘ShardUpload 441’ in FIG. 4) configured to upload shard files 432 to clients.For example, shard uploader 441 may be configured to upload shard files432 to a cloud storage 450 (depicted ‘Cloud Storage 450’ in FIG. 4).Computer environments, such as computer environment 320 a-320 c, CIservers, such as CI server 310, and/or autonomous vehicles, such asautonomous vehicle 110, may access or download the shards from the cloudstorage 450. In another example, shard uploader 441 may be configured toupload shard files 432 to a computer environment, e.g., map storage 322a-322 c of computer environment 322 a-322 c, to a CI server, e.g., mapstorage 312 of CI server 310, and/or to an autonomous vehicle, e.g., mapstorage 106 of autonomous vehicle 110.

Shard metadata 433 may be uploaded to clients. The shard metadata 433may be uploaded by the uploader 440 to cloud storage 450 or to anothercloud storage 460 (depicted ‘Cloud Storage 460’ in FIG. 4) for separatestorage.

In some embodiments, the uploader 440 may be further configured toupload monolithic map data to a client, e.g., in case the client is notcompatible with shard data or in case shard data is not available in ageographical area.

As shown in the example of FIG. 5, a cloud storage 450 a may store sharddata 451-453 for each of the clients, e.g., autonomous vehicles 210 dand computer environments 320 d-320 e. To reduce the load on the cloudstorage and network connections to the cloud storage, for example whenoffering dynamic loading of shard data, a cloud storage 450 b may storeshard data 454 that is shared by multiple clients, as shown in theexample of FIG. 6. For example, computer environments 320 f-320 g mayshare shard data 454. In the example of FIG. 6, dedicated shard data 455is available to client 320 h.

Shard Guiding Sections

The shard guiding sections, such as the sections stored in the shardguiding database 412 of FIG. 4, may be obtained by splitting ageographical area into sections. A geographical area may be split intosections in various manners.

FIG. 7 shows an example of a geographical area 500, in this example acity, which has been split into sections, depicted by the dashed lines.Only three sections have been labeled in FIG. 7, i.e., section 501,section 502 and section 503. Sections can have different shapes andsizes. For example, section 501 is smaller and differently shapedcompared to section 502. Section 503 is an example of an elongatedsection. Sections are typically non-overlapping, but it is possible thatthere are overlapping sections. It is possible that parts of thegeographical area 500 are excluded from generating shards. In FIG. 7,the area 510 is an example of a part of the geographical area 500 thatis not part of a section and will not be covered by a shard, e.g.,because that part of the city cannot be reached by an autonomousvehicle.

One or more of the sections may be defined such that semantic objectswith geometries located in the geographical area fit into the sections,possibly with small overlap to adjacent sections. Objects that overlapto adjacent sections may expand the bounding box of the shards.

The geographical area 500 may be split into sections differently, e.g.,in larger sections in one area and in smaller sections in another area.Sections, and in particular smaller sections, preferably excludeundriveable areas in between roads on city blocks. Larger sectionscover, e.g., neighborhoods of a city or smaller villages.

The above-described sections are to be understood as non-limitingexamples of sections. Any other split of a geographical area intosections may be used as input to the map sharder 401 for the generationof semantic map shards.

In some embodiments, a computer algorithm may be used to split ageographical area into sections. In one example, the computer algorithmmay be configured to recognize geographical features in a geometricmapping layer of the geographical area and define sections accordingly.In another example, the computer algorithm may be configured todetermine a density of semantic objects at a location, e.g., using thesemantic objects database 411, and define sections accordingly. Thecomputer algorithm may include artificial intelligence algorithms torecognize features and/or objects in map data of a geographical area insupport of splitting the geographical area into sections.

In some embodiments, the computer algorithm for splitting a geographicalarea into sections may use a set of rules as input. FIG. 8 shows anexample flow diagram 600 of a rules based splitting of a geographicalarea into sections by a computer algorithm. The sections may be storedin shard guiding sections database 412.

A set of rules 601 may be used to determine which semantic objects 602are to be kept together in a shard. The rules 601 may be part of thecomputer algorithm 610 (depicted ‘Splitting 610’ in FIG. 8) or storedseparately and addressed by the computer algorithm 610. The semanticobjects may be obtained from the semantic objects database 411.

Based on the set of rules 601 and the semantic objects 602, thealgorithm 610 may determine an optimal split of the geographical area603 into sections 605. Herein, the geographical area may be input to thealgorithm 610 as map data representative of the geographical area.

The algorithm 610 optionally uses an optimization metric 604 as furtherinput to tune the algorithm 610 for optimal generation of sections 605.For example, the optimization metric 604 may instruct the algorithm togenerate sections 605 that may result in shards that are optimized formemory usage, i.e., memory usage when a client processes the shard.Another example of an optimization metric defines a desired data storagesize per shard, and results in sections 605 to be generated accordingly.Another example of an optimization metric defines a desired objectdensity or number of objects within a section 605.

Generating Shard Data

As explained in the above example of FIG. 3, from the semantic objects,e.g., stored in semantic objects database 411, and the sections, e.g.,stored in shard guiding sections database 412, the map sharder 401 maygenerate shard data. FIG. 9 shows a flow diagram 700 of a method forgenerating shard data 704, according to some embodiments.

The example of FIG. 9 shows data representative of a geographical area701, which is input to a splitting function 710 (depicted ‘Splitting710’ in FIG. 9). Output of the splitting function 710 are sections 702.A map sharder function 720 takes the sections 702 as input, togetherwith semantic objects 703. The map sharder function 720 may generate theshard data 704 based on the sections 702 and semantic objects 703.

The data representative of a geographical area 701 may be split bysplitting function 710 into sections 702. An example of a splittingfunction 710 is the splitting algorithm 610 of FIG. 8. An example ofsections obtained by the splitting function are the sections 605 of FIG.8. The thus obtained sections 702 may be stored, e.g., in shard guidingsections database 412.

The semantic objects 703 may be obtained from semantic objects database411.

The map sharder function 720 may generate shard data 704 by looking upthe semantic objects that are located in a section. The map sharderfunction 720 may furthermore calculate the bounding box for the shardcovering these objects. The map sharder function 720 may be performed bymap sharder 401.

In some embodiments, the map sharder function 720, may involve a numberof sub steps, such as shown in the flow diagram 7200 of FIG. 10.

In step 7210 (depicted ‘Apply Rules 7210’ in FIG. 10) it may bedetermined which objects can be stored together within a shard based onrules 7201. Rules 7201 may be similar or identical to rules 601.

In step 7220 (depicted ‘Objects in Sections 7220’ in FIG. 10) it may bedetermined which objects are located within a section. Hereto thegeographical area covered by a section may be determined from thesection, e.g., from a geographical reference stored with the section.The semantic objects database 411 may be searched for objects that arelocated within the geographical area covered by the section, e.g., froma geographical reference stored with each object in the semantic objectsdatabase 411.

In step 7230 (depicted ‘Store in Shard 7230’ in FIG. 10), objects thatare found in step 7220 may be stored in a shard. The thus created shardmay be stored in the shard-to-object relations database 414.

In step 7240 (depicted ‘Calculate Bounding Box 7240’ in FIG. 10) thebounding box may be calculated for a shard. The bounding box isindicative of the geographical area covered by the shard, which may bedifferent from the geographical area of the section, as the object inthe shard may be larger or smaller than the section. Step 7240 may beperformed each time a shard has been created in step 7230.Alternatively, step 7240 may be performed when a plurality of shards,possibly all shards, have been created in step 7230, resulting in thebounding boxes being calculated for the plurality of shards in step7230.

In step 7250 (depicted ‘Store Bounding Box 7250’ in FIG. 10) dataindicative of the bounding box may be stored with the shard. Thebounding box data may be stored as metadata, e.g., in shard metadatadatabase 413.

In addition to generating shards based on the sections, shards may begenerated covering larger areas, e.g., resulting in city-wide shards.These shards covering a larger area may be used for specific use casesor for specific type of objects.

As will be appreciated by one skilled in the art, the steps shown inFIG. 9 or FIG. 10 may be performed sequentially and/or in parallel fordifferent shards to be generated. Steps may be repeated for differentshards.

Rules for Placing Semantic Objects into Shards

A shard may include a single object or multiple objects. When generatingshards, a set of rules, such as rules 7201, may be applied to determinewhich objects may be stored together in a shard.

For example, for map consistency reasons, lanes and their boundariesand/or lanes and their intersection may be stored together in a shard.As used herein, a lane is a portion of a roadway along which anautonomous vehicle can travel. A lane may be described by itsboundaries, including a left boundary and a right boundary, and adirection of travel. In one example, a lane includes a portion of aroadway physically marked by painted lane lines, a median, reflectivemarkers, or other demarcations. In another example, a lane includes aportion of a roadway on which autonomous vehicles travel by convention,e.g., the right-hand portion of a two-way street without a lanedemarcation. In another example, a lane includes the full width of aroadway, e.g., for a single lane road. A lane may have start points andend points, e.g., a lane may start at one end of a given city block andend at the other end of the city block; a corresponding lane on the nextcity block may be considered a different lane, or a continuation of thesame lane. A lane may extend through an intersection, or a portion of aroadway through an intersection may be considered a distinct lane from acorresponding portion before the intersection. In some examples,multiple lanes overlap. For example, at an intersection, each potentialpathway for traveling through the intersection, including straightpathways and turning pathways, is considered a lane. As another example,in a rural area, a single lane road may allow travel in two directions,and the full width of the road in either direction of travel isconsidered a separate lane.

The rules 7201 may define objects with geometries and/or relationshipsbetween objects that may be stored together in a shard. Objects withgeometries are typically affecting the shard bounding box, resulting inthe bounding box being calculated and stored for the shard.Relationships between objects typically do not affect the shard boundingbox and need not be taken into account when calculating a bounding box.The following are non-limiting examples of rules 7201.

The rules 7201 may define primary objects, that may be stored togetherin a shard. For example, it may be defined that closely related primaryobjects are stored together in a shard. Closely related primary objectsare, e.g., lanes, boundaries and intersections that can be groupedtogether based on lane to boundary relationships and intersectionidentifications. These groups may form chunks that cannot be split intoseparate shards, as these objects are typically to be processed togetherby a client. These groups may preserve basic guarantees that a lanealways has a boundary and intersection data is available to a client inthe map for every lane. When generating shards, the geometries of agroup of primary objects may be tested against sections, based on whichthe primary objects may be selected and stored in the shards.

The rules 7201 may define objects related directly to one and only oneof the primary objects. If an object has a relationship to exactly oneof the primary objects, the rules 7201 may define that the object may beplaced in the same shard with that primary object. The relationship tothe primary object may also be stored in the shard. Non-limitingexamples of objects directly related to primary objects are: lane grade(related to a lane); conditional speed limit (related to a lane); andcross traffic lanes (related to an intersection).

The rules 7201 may define objects with large geometries or no relationsto other objects that cannot be reasonably placed in one of thesections. These objects may be separated into their own shard pergeographical area, e.g., in a shard covering a city instead of asection. Non-limiting examples of such objects are: a routable area; aschool zone driveable area; and experimental areas for simulationpurposes.

The rules 7201 may define objects with geometries, which may be added tothe shards of the sections that contain most of that geometry. If two ormore shards share equal parts, a tiebreaker may be implemented to ensurethat the sharding is repeatable. Non-limiting examples of such objectswith geometries are: a driveable area; an undriveable area; a rail line;a traffic sign; and a traffic light.

The rules 7201 may define objects closely related to an object withgeometry, which may be placed into a shard together with the geometricobject. The relationship with the geometrical object may also be storedin the shard. Non-limiting examples of objects closely related toobjects with geometry are: a buffered crosswalk area (related to adriveable area); and a driveway entry (related to a driveway driveablearea).

The rules 7201 may define relationships between objects that can be indifferent shards. These relationships are preferably stored with one ofthe objects. Non-limiting examples of such relationships are: laneconnection (related to a start lane); and lane driveable areaintersections (related to a lane).

Data Structure of a Shard

In some embodiments, the generated shard data, such as shard data 704,may be stored as database tables in a database. The shard data 704 maybe stored in two tables: a shard metadata table, such as shard metadatadatabase 413, and a shard relations table, such as shard-to-objectrelations database 414.

In an example, the shard metadata database 413 may include one or moreof the following fields:

-   -   id, i.e., an identifier of the shard that may be used as foreign        key in the shard-to-object relations database 414. The id is for        example stored as an integer value;    -   subtype, i.e., a non-unique string representing the type of        shard. The subtype is for example stored as a string value;    -   name, i.e., a unique string representing the name of the shard.        The name is for example stored as a string value;    -   {min_x, min_y, max_x, max_y}, i.e., four values defining the        corners of a bounding box. These values are for example stored        as floating-point values.

The subtype field in the shard metadata database 413 may be populatedbased on whether a shard contains data about objects related to sections(e.g., stored as subtype=“small” in case of being related to a smallsection), city-wide objects (e.g., stored as subtype=“city”) or objectsfor experiments for simulation purposes (e.g., stored assubtype=“experiment”). Other subtype values may be used for these and/orother types of shards.

The name of the shard in the shard metadata database 413 may beconstructed from the subtype and a sequence number. For example, thefollowing names may be given to shards: small_1, small_2, small_3, andetcetera. Other naming schemes may be used to uniquely name the shards.

The shard-to-object relations database 414 may store informationidentifying the objects stored in the shards. This information mayinclude relations between shards and objects as may be defined by therules 7201.

In an example, the shard-to-object relations database 414 may includeone or more of the following fields:

-   -   shard_id, i.e., a foreign key to the shard relations table. The        shard_id is for example stored as an integer value;    -   feature_table, i.e., a name of the feature table or a name        identifying the shard. The feature_table is for example stored        as a string value;    -   feature_id, i.e., an identifier of the feature or objects in the        feature table. The feature_id is for example stored as an        integer value.

Exporting Shard Data from a Database Storage

Preferably, the shard data stored in the database storage is exportedfor storage outside of the database before being used by a client. FIG.11 shows a flow diagram 800 of a method for exporting shard data fromthe database, according to some embodiments. Shard data 810 stored inthe database, e.g., shard data 704 stored as shard metadata database 413and shard-to-object relations database 414 in database storage 410, maybe retrieved from the database by an export function 801 (depicted‘Export 801’ in FIG. 11). The exporting function 801 may be executed bydatabase exported 420. Based on the shard data retrieved from thedatabase, the exporting function 801 may generate shard files 811 thatare stored in a file storage system, such as file storage system 430.Each shard file preferably stores data for one shard. Shard metadata maybe stored in one or more separate files with the shard files 811.

In some embodiments, the export function 801 creates a shards directorycontaining the shard files and a metadata file, e.g., a json file“metadata.json”. The metadata file may include a list of shard filenames with their respective subtype and bounding box information. Thismetadata information may be used by an uploader function, such asuploader 440, to upload shard metadata and the shard files to clients ora cloud storage.

The following is an example of an entry in the metadata.json file forone shard: {“name”: “meta_1.bp”, “subtype”: “meta”, “min_x”: −100.0,“min_y”: −200.1, “max_x”: 145.2, “max_y”: −100.4}. Herein, the name,subtype, min_x, min_y, max_x and max_y fields may be given the valuesfrom the corresponding fields in the shard metadata database 413. Thename field in the metadata.json file may furthermore include a filenameextension, such as “.bp” in this example. The full name in themetadata.json file may thus refer to the filename “meta_1.bp” of theshard.

In some embodiments, the shard files 811 may be uploaded by an uploaderfunction 802 to a client or cloud storage before being used by a client,which is shown in flow diagram 850 of FIG. 12. The uploader function maybe performed by shard uploader 441.

In step 802 (depicted ‘Upload 802’ in FIG. 12) the shard files 811 maybe read and uploaded to a cloud storage 812. The cloud storage mayinclude a content addressable storage (CAS) for storing the shards forperformance enhancements. Shards and shard meta data may be storedseparately, possibly in separate cloud locations, such as shown in theexample of FIG. 4.

In some embodiments, the shard files 811 and metadata include a hashvalue of the identifier of the shard, e.g., a SHA256 identifier.

Selection and Delivery of Shards to a Client

FIG. 13 shows a flow diagram 900 of a method for selection and deliveryof shard data to a client, according to some embodiments.

In step 901 (depicted ‘Receive Filter Data 901’ in FIG. 13) filter datamay be received from a client indicative of the map data that isrequested by the client. The filter data may include bounding boxrelated information indicative of the geographical area for which shardsare requested. The filter may include further filter information.Non-limiting examples of further filter information are data indicativeof a specific path and information for selecting a specific layer of mapdata, e.g., only primary objects.

In step 902 (depicted ‘Load Shard Metadata and get Shards 902’ in FIG.13), shard metadata may be loaded, e.g., from the metadata.json file ofthe example of FIG. 11 or from metadata stored in a cloud storage 460 ofFIG. 4. The filter data, in particular the bounding box informationprovided in the filter data, may be correlated against the bounding boxdata in the shard metadata to find the relevant shard files. The shardsfulfilling the request from the client may be returned to the client instep 903 (depicted ‘Return Shards 903’ in FIG. 13).

For example, the bounding box information provided in the filter datamay indicate a request for shards in an area defined by min_x=−90,min_y=−200, max_x=144 and max_y=−110. The metadata for small_1.bp in theexample above covers this area, with a bounding box of min_x=−100.0,min_y=−200.1, max_x=145.2 and max_y=−100.4. Step 902 may thus result inshard data in the form of file small_1.bp being returned to the client.

Step 902 may include one or more sub steps, indicated as dashed boxes9021, 9022 and 9023 in FIG. 13. In step 9021 (depicted ‘Get Bounding Box9021’ in FIG. 13) the filter data may be analyzed and bounding boxinformation may be obtained from the client request. If a bounding boxis present, shards that do not overlap with the bounding box may beexpanded by a configurable padding or buffer zone in step 9022 (depicted‘Padding 9022’ in FIG. 13). This effectively temporarily enlarges theareas covered by these shards. Padding may be required, e.g., to accountfor the distance between the autonomous vehicle and the farthest objectthat affects autonomous vehicle behavior. In step 9023 (depicted ‘SelectShards 9023’ in FIG. 13) the shards may be selected based on the filterdata and the expanded bounding boxes.

In some embodiments, shards may not be available in an area requested bythe filter data or shards may not be supported by one or more of theclients. FIG. 14 shows a flow diagram 910 of a method for supportingdelivery of a monolithic map data in such cases. In step 911 a requestfor map data may be received from a client. In step 912 it may bedetermined whether shards are supported by the client and shard data isavailable in the requested area. If shards are available and supported,the flow diagram 900 may be followed. Otherwise, in step 913 monolithicmap data may be returned. It is possible that at a later stage it isdetermined that shard data is not available, e.g., in step 902 of FIG.13. Also, in that case monolithic map data may be returned to the clientinstead of shard data.

Dynamic Loading of Shards by Clients

All shard data required by a client, e.g., to perform a ride from startto destination or to perform software simulations, may be delivered tothe client at once after a request is received by a client.Alternatively, shard data may be loaded dynamically, allowing onlyshards that are currently used to be requested and delivered to clientsand further shards to be delivered when needed. For example, anautonomous vehicle traveling 20 km from start to destination may requestshards for an area of 500 m around the vehicle at a time, onlyrequesting further shards when reaching 250 m from the edge of the 500 marea. This dynamic loading of shards of radially 500 m areas around thevehicle may continue until the 20 km ride has finished.

FIG. 15 shows a flow diagram 920 of a method for dynamic loading ofshard data, according to some embodiments. In step 921 a request forshards may be received from a client. In step 922 (depicted ‘ReturnShards 922’ in FIG. 15) the shards that are currently needed may befound and returned to the client, e.g., using a process similar to step902 in FIG. 13. The method 920 may be repeated to provide further shardsto the client when needed.

In an embodiment, the process of dynamic loading of shards may beoptimized by predicting which shards will be requested by the clientnext. The predicted shards may be preloaded to minimize latency betweenthe request from the client and delivery of the next shards. In step 923(depicted ‘Predict next Shards 923’ in FIG. 15) it may be predictedwhich shard data will be requested next, e.g., based on a direction ofdriving or based on an end-to-end route if known. Step 923 may result inthe next shards to be preloaded.

In an example, shards that are required for a specific trip may bepreloaded for a client and the client may request shard loading whilestationary after calculating the route for a next trip.

Example of a Computing System for Semantic Map Sharding

FIG. 16 shows an example embodiment of a computing system 1000 forimplementing certain aspects of the present technology. In variousexamples, the computing system 1000 can be any computing device makingup the onboard computer 104, the central computer 202, onboard computer220 a-220 c, CI server 310, computer environment 320 a-320 c, sourcecontrol server 330, any part of the system architecture 400, such as mapsharder 401, database storage 410, database exporter 420, file storagesystem 430, uploader 440, or any other computing system describedherein.

In some implementations, a computing system 1000 can implement themethods described herein, such as to generate shards.

The computing system 1000 can include any component of a computingsystem described herein which the components of the system are incommunication with each other using connection 1005. The connection 1005can be a physical connection via a bus, or a direct connection intoprocessor 11010, such as in a chipset architecture. The connection 1005can also be a virtual connection, networked connection, or logicalconnection.

In some implementations, the computing system 1000 is a distributedsystem in which the functions described in this disclosure can bedistributed within a datacenter, multiple data centers, a peer network,etc. In some embodiments, one or more of the described system componentsrepresents many such components each performing some or all of thefunctions for which the component is described. In some embodiments, thecomponents can be physical or virtual devices.

The example system 1000 includes at least one processing unit (CPU orprocessor) 1010 and a connection 1005 that couples various systemcomponents including system memory 1015, such as read-only memory (ROM)1020 and random-access memory (RAM) 1025 to processor 1010. Thecomputing system 1000 can include a cache of high-speed memory 1012connected directly with, in close proximity to, or integrated as part ofthe processor 1010.

The processor 1010 can include any general-purpose processor and ahardware service or software service, such as services 1032, 1034, and1036 stored in storage device 1030, configured to control the processor1010 as well as a special-purpose processor where software instructionsare incorporated into the actual processor design. The processor 1010may essentially be a completely self-contained computing system,containing multiple cores or processors, a bus, memory controller,cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction, the computing system 1000 includes an inputdevice 1045, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Thecomputing system 1000 can also include an output device 1035, which canbe one or more of a number of output mechanisms known to those of skillin the art. In some instances, multimodal systems can enable a user toprovide multiple types of input/output to communicate with the computingsystem 1000. The computing system 1000 can include a communicationsinterface 1040, which can generally govern and manage the user input andsystem output. There is no restriction on operating on any particularhardware arrangement, and therefore the basic features here may easilybe substituted for improved hardware or firmware arrangements as theyare developed.

A storage device 1030 can be a non-volatile memory device and can be ahard disk or other types of computer readable media which can store datathat are accessible by a computer, such as magnetic cassettes, flashmemory cards, solid state memory devices, digital versatile disks,cartridges, random access memories (RAMS), read-only memory (ROM),and/or some combination of these devices.

The storage device 1030 can include software services, servers,services, etc., that when the code that defines such software isexecuted by the processor 1010, it causes the system to perform afunction. In some embodiments, a hardware service that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as a processor 1010, a connection 1005, an outputdevice 1035, etc., to carry out the function.

Select Examples

The following paragraphs provide various examples of the embodimentsdisclosed herein.

Examples of Systems and Methods for Generating Shard Data Based onSemantic Map Sharding

Example 1 provides a system for generating shard data. The system mayinclude a map sharder configured to generate shard data from a firstdata input including semantic objects in a geographical area and asecond data input including a definition of sections. The sections maycover parts of the geographical area. The map sharder may be configuredto generate the shard data by searching, for each of the sections, forsemantic objects that are located at least partly in a section and tostore found objects in a shard data entry of the shard data.

Example 2 provides a system according to example 1, wherein the mapsharder is further configured to calculate a bounding box for the sharddata entry based on the objects stored in the shard data entry. The mapsharder may further be configured to store data representative of thebounding box with the shard data entry.

Example 3 provides a system according to example 2, wherein the mapsharder is configured to calculate bounding boxes for all shard dataentries after generating all shard data entries.

Example 4 provides a system according to example 2 or example 3, whereinthe shard data includes a first database configured to store metadatafor each of the shard data entries. The metadata may include the datarepresentative of the bounding box. The shard data may further include asecond database configured to store data indicative of objects locatedin each of the shard data entries.

Example 5 provides a system according to any one of the examples 1-4,wherein the map sharder is configured to generate the shard data furtherbased on rules. The rules may include a definition of which semanticobjects are to be combined into a single shard data entry and whichsemantic objects are to be stored in separate shard data entries.

Example 6 provides a system according to example 5, wherein the rulesdefine one or more of: closely related primary objects, which are to begrouped together in a single shard; objects related to one or moreprimary objects, which are to be stored in the same shard data entry asthe related primary object; objects with large geometries or norelations to other objects that cannot be reasonably placed in one ofthe sections, which are to be stored in separate shard data entries;objects with geometries, which are to be added to a shard entry of asection that contains most of that geometry; objects closely related toan object with geometry, which are to be stored into a shard data entrytogether with the geometric object; and relationships between objects indifferent shards data entries, which are to be stored with one of theobjects.

Example 7 provides a system according to any one of the examples 1-6,further including a database exporter. The database exporter may beconfigured to generate shard data files from the shard data. One sharddata file typically includes data for one shard data entry. The systemmay further include a file storage system. The file storage system maybe configured to store the shard data files.

Example 8 provides a system according to example 7, wherein the databaseexporter is further configured to generate a metadata file. The metadatafile may include bounding box information for the shard data entries.The file storage system may further be configured to store the metadatafile.

Example 9 provides a system according to example 7 or example 8, whereinthe database exporter is further configured to generate monolithic mapdata. The file storage system may further be configured to store themonolithic map data.

Example 10 provides a system according to any one of the examples 7-9,further including an uploader. The uploader may be configured to uploadthe shard data files to a cloud storage.

Example 11 provides a system according to example 8, further includingan uploader. The uploader may be configured to upload the metadata fileto a cloud storage.

Example 12 provides a method for generating shard data. The method mayinclude receiving, in a map sharding function, a first data inputincluding semantic objects in a geographical area. The method mayfurther include receiving, in the map sharding function, a second datainput including a definition of sections. The sections may cover partsof the geographical area. The method may further include searching, bythe map sharding function, for each of the sections, for semanticobjects that are located at least partly in a section. The method mayfurther include storing, by the map sharding function, found objects ina shard data entry of the shard data.

Example 13 provides a method according to example 12, further includingcalculating, by the map sharding function, a bounding box for the sharddata entry based on the objects stored in the shard data entry. Themethod may further include storing, by the map sharding function, datarepresentative of the bounding box with the shard data entry.

Example 14 provides a method according to example 13, wherein the datarepresentative of the bounding box is stored as metadata separate fromthe shard data entries.

Example 15 provides a method according to any one of the examples 12-14,further including receiving, in the map sharding function, rules. Therules may include a definition of which semantic objects are to becombined into a single shard data entry and which semantic objects areto be stored in separate shard data entries. The searching of semanticobjects may further be based on the rules.

Example 16 provides a method according to any one of the examples 12-15,further including exporting, by an export function, the shard data to afile storage system by storing each shard data entry as a separate datafile in the file storage system.

Example 17 provides a method according to example 14, further includingexporting, by the export function, the metadata to a file storagesystem.

Example 18 provides one or more non-transitory computer-readable storagemedia including instruction for execution which, when executed by aprocessor, are operable to perform operations for generating shard data.The operations may include inputting a first data input includingsemantic objects in a geographical area. The operations may furtherinclude inputting a second data input including a definition ofsections. The sections may cover parts of the geographical area. Theoperations may further include, for each of the sections in the seconddata input, searching the first data input for semantic objects that arelocated at least partly in the section to obtain found objects. Theoperations may further include outputting data representative of foundobjects for storage in a shard data entry of the shard data.

Example 19 provides one or more non-transitory computer-readable storagemedia according to example 18, wherein the operations further includecalculating a bounding box for the shard data entry based on the foundobjects for the shard data entry. The operations may further includeoutputting data representative of the bounding box for storage with theshard data entry.

Example 20 provides one or more non-transitory computer-readable storagemedia according to example 18 or example 19, wherein the operationsfurther include inputting rules. The rules may include a definition ofwhich semantic objects are to be combined into a single shard data entryand which semantic objects are to be stored in separate shard dataentries. The searching the first data input for semantic objects mayfurther be based on the rules.

Examples of Systems and Methods for Delivering Shard Data to a Client

Example 21 provides a method for delivering shard data to a client. Themethod may include receiving filter data from the client. The filterdata may be indicative of which map data is requested by the client. Themethod may further include searching for shards that fulfill the filterdata. The method may further include returning the shards that fulfillthe filter data to the client.

Example 22 provides a method according to example 21, wherein the filterdata includes data indicative of a bounding box. The searching of shardsmay use the data indicative of the bounding box to find shards that atleast partly overlap the bounding box.

Example 23 provides a method according to example 22, wherein shardsthat do not overlap with the bounding box are expanded in size by apredefined padding. The expanded shards may be used to find shards thatat least partly overlap the bounding box.

Example 24 provides a method according to any one of the examples 21-23,further including returning monolithic map data to the client instead ofshards in case no shards are found that fulfill the filter data or incase the filter data indicates that shards are not supported.

Example 25 provides a system for delivering shard data to a client. Thesystem may be configured to perform the method according to any one ofthe examples 21-24.

Examples of Systems and Methods for Dynamic Loading of Shard Data

Example 26 provides a system for dynamically loading shards. The systemmay include a receiver configured to receive a request for shard datafrom a client. The system may further include a processor configured tosearch for shards that are currently needed by the client based on therequest. The system may further include a transmitter configured toreturn the found shards to the client. The system may be configured torepeatedly receive requests from the client and provide shards to theclient.

Example 27 provides a system according example 26, wherein the processoris configured to preload shards based on a direction of driving derivedfrom previously returned shards or based on data indicative of anend-to-end route provided by the client. The transmitter may beconfigured to return a preloaded shard.

Example 28 provides a system according to example 26 or example 27,further including a dedicated data storage dedicated to the client. Thededicated data storage may store shard data from where shards aresearched to be returned to the client.

Example 29 provides a system according to any one of the examples 26-28,further including a shared data storage that is shared between two ormore clients. The shared data storage may store shard data from whereshards are searched to be returned to the client.

Example 30 provides a method for dynamically loading shards. The methodmay include receiving a request for shard data from a client. The methodmay further include searching for shards that are currently needed bythe client based on the request. The method may further includetransmitting the found shards to the client. The method may furtherinclude repeating these steps for further requests from the client.

Example 31 provides a method according to example 30, wherein the methodfurther includes preloading shards based on a direction of drivingderived from previously returned shards or based on data indicative ofan end-to-end route provided by the client. The method may furtherinclude transmitting a preloaded shard to the client.

Examples of Method for Generating Shard Data from a Geographical AreaSplit in Sections

Example 32 provides a method for generating shard data using datarepresentative of a geographical area that is split into sections anddata representative of semantic objects located in the geographicalarea. The method may include searching, for each of the sections, forsemantic objects that are located at least partly in a section. Themethod may further include generating a shard data entry of the sharddata. The shard data entry may include one or more semantic objectsfound by the searching.

Example 33 provides a method according to example 32, wherein searchingand generating are repeated for all sections.

Example 34 provides a method according to example 33, wherein, after allshard data entries have been generated, a semantic object is located inonly one shard data entry.

Example 35 provides a method according to example 33 or example 34,wherein, after all shard data entries have been generated, all semanticobjects are contained in the shard data.

Example 36 provides a method according to any one of the examples 32-35,wherein the geographical area covers one of a city, a region or acountry.

Example 37 provides a method according to any one of the examples 32-36,wherein the data representative of the geographical area is split intosections. A section may be defined such that semantic objects withgeometries located in the geographical area fit into the section,possibly with small overlap to one or more adjacent sections.

Example 38 provides a method according to any one of the examples 32-37,wherein the method may further use rules. The rules may include adefinition of which semantic objects are to be combined into a singleshard data entry and which semantic objects are to be stored in separateshard data entries. The searching may use the rules to find semanticobjects that fulfill the rules.

Example 39 provides a method according to example 38, wherein the rulesare defined to include one or more of: closely related primary objects,which are to be grouped together in a single shard; objects related toone or more primary objects, which are to be stored in the same sharddata entry as the related primary object; objects with large geometriesor no relations to other objects that cannot be reasonably placed in oneof the sections, which are to be stored in separate shard data entries;objects with geometries, which are to be added to a shard entry of asection that contains most of that geometry; objects closely related toan object with geometry, which are to be stored into a shard data entrytogether with the geometric object; and relationships between objects indifferent shards data entries, which are to be stored with one of theobjects.

Example 40 provides a method according to example 39, wherein theprimary objects include one or more of: lanes; boundaries; andintersections that can be grouped together based on lane to boundaryrelationships and intersection identifications.

Example 41 provides a method according to example 39 or example 40,wherein the objects related to one or more primary objects include oneor more of: lane grade (related to a lane); conditional speed limit(related to a lane); and cross traffic lanes (related to anintersection).

Example 42 provides a method according to any one of the examples 39-41,wherein the objects with large geometries or no relations to otherobjects include one or more of: a routable area; a school zone driveablearea; and experimental areas for simulation purposes.

Example 43 provides a method according to any one of the examples 39-42,wherein the objects with geometries include one or more of: a driveablearea; an undriveable area; a rail line; a traffic sign; and a trafficlight.

Example 44 provides a method according to any one of the examples 39-43,wherein the objects closely related to an object with geometry includeone or more of: a buffered crosswalk area (related to a driveable area);and a driveway entry (related to a driveway driveable area).

Example 45 provides a method according to any one of the examples 39-44,wherein the relationships between objects in different shards dataentries include one or more of: lane connection (related to a startlane); and lane driveable area intersections (related to a lane).

Example 46 provides one or more non-transitory computer-readable storagemedia, including instruction for execution which, when executed by aprocessor, are operable to perform operations for generating shard data.The operations may include loading data representative of a geographicalarea that has been split into sections. The operations may furtherinclude loading data representative of semantic objects in thegeographical area. The operations may further include searching, foreach of the sections, for semantic objects that are located at leastpartly in a section. The operations may further include outputting ashard data entry of the shard data. The shard data entry may includedata representative of one or more semantic objects found by thesearching.

Example 47 provides one or more non-transitory computer-readable storagemedia according to example 46, wherein the searching and outputting arerepeated for all sections that are loaded with the loading of datarepresentative of the geographical area that has been split intosections.

Example 48 provides one or more non-transitory computer-readable storagemedia according to example 46 of example 47, wherein the operationsfurther include loading data representative of rules. The rules mayinclude a definition of which semantic objects are to be combined into asingle shard data entry and which semantic objects are to be stored inseparate shard data entries. The searching may use the rules to findsemantic objects that fulfill the rules.

Example 49 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 46-48, wherein the rules aredefined to include one or more of: closely related primary objects,which are to be grouped together in a single shard; objects related toone or more primary objects, which are to be stored in the same sharddata entry as the related primary object; objects with large geometriesor no relations to other objects that cannot be reasonably placed in oneof the sections, which are to be stored in separate shard data entries;objects with geometries, which are to be added to a shard entry of asection that contains most of that geometry; objects closely related toan object with geometry, which are to be stored into a shard data entrytogether with the geometric object; and relationships between objects indifferent shards data entries, which are to be stored with one of theobjects.

Example 50 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 46-49, wherein a section isdefined such that semantic objects with geometries located in thegeographical area fit into the section, possibly with small overlap toone or more adjacent sections.

Example 51 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 46-50, wherein, after allshard data entries have been generated, all semantic objects arecontained in the shard data.

Examples of Methods for Splitting a Geographical Area into Sections forSemantic Map Sharding

Example 52 provides a method for generating sections. The method mayinclude receiving data representative of semantic objects in ageographical area. The method may further include receiving a set ofrules. The rule may include a definition of which semantic objects areto be combined into a single shard data entry and which semantic objectsare to be stored in separate shard data entries. The method may furtherinclude analyzing data representative of the geographical area todetermine an optimal split of the geographical area into sections basedon the semantic objects and the rules, and generating the sectionsaccordingly.

Example 53 provides a method according to example 52, further includingreceiving an optimization metric. The optimization metric may includeinstructions for one or more of: generate shards that are optimized formemory usage; a desired data storage size per shard; and a desiredobject density or number of objects within an area that may be stored ina shard. The analyzing may further be based on the optimization metric.

Example 54 provides a method according to example 52 or example 53,wherein the geographical area covers one of a city, a region or acountry.

Example 55 provides a method according to any one of the examples 52-54,wherein the set of rules includes one or more of the followingdefinitions: closely related primary objects are to be grouped togetherin a single shard; objects related to one or more primary objects are tobe stored in the same shard data entry as the related primary object;objects with large geometries or no relations to other objects thatcannot be reasonably placed in one of the sections are to be stored inseparate shard data entries; objects with geometries are to be added toa shard entry of a section that contains most of that geometry; objectsclosely related to an object with geometry are to be stored into a sharddata entry together with the geometric object; and relationships betweenobjects in different shards data entries are to be stored with one ofthe objects.

Example 56 provides a method according to example 55, wherein theprimary objects include one or more of: lanes; boundaries; andintersections that can be grouped together based on lane to boundaryrelationships and intersection identifications.

Example 57 provides a method according to example 55 or example 56,wherein the objects related to one or more primary objects include oneor more of: lane grade (related to a lane); conditional speed limit(related to a lane); and cross traffic lanes (related to anintersection).

Example 58 provides a method according to any one of the examples 55-57,wherein the objects with large geometries or no relations to otherobjects include one or more of: a routable area; a school zone driveablearea; and experimental areas for simulation purposes.

Example 59 provides a method according to any one of the examples 55-58,wherein the objects with geometries include one or more of: a driveablearea; an undriveable area; a rail line; a traffic sign; and a trafficlight.

Example 60 provides a method according to any one of the examples 55-59,wherein the objects closely related to an object with geometry includeone or more of: a buffered crosswalk area (related to a driveable area);and a driveway entry (related to a driveway driveable area).

Example 61 provides a method according to any one of the examples 55-60,wherein the relationships between objects in different shards dataentries include one or more of: lane connection (related to a startlane); and lane driveable area intersections (related to a lane).

Examples of Methods for Rules Based Semantic Map Sharding

Example 62 provides a method of handling semantic objects for semanticmap sharding. The method may include defining semantic objects in ageographical area to be contained in shard data. The method may furtherinclude determining for the semantic objects a set of rules. The rulesmay include a definition of which semantic objects are to be combinedinto a shard data entry of the shard data and which semantic objects areto be stored in separate shard data entries.

Example 63 provides a method according to example 62, wherein thesemantic objects include one or more of: primary objects; objectsrelated to one or more primary objects; objects with large geometries orno relations to other objects; objects with geometries; and objectsclosely related to an object with geometry. The rules may includedefinitions for one or more of: closely related primary objects, whichare to be grouped together in a single shard; objects related to one ormore primary objects, which are to be stored in the same shard dataentry as the related primary object; objects with large geometries or norelations to other objects that cannot be reasonably placed in onesections, which are to be stored in separate shard data entries; objectswith geometries, which are to be added to a shard entry of a sectionthat contains most of that geometry; objects closely related to anobject with geometry, which are to be stored into a shard data entrytogether with the geometric object; and relationships between objects indifferent shards data entries, which are to be stored with one of theobjects.

Example 64 provides a method according to example 63, wherein theprimary objects include one or more of: lanes; boundaries; andintersections that can be grouped together based on lane to boundaryrelationships and intersection identifications.

Example 65 provides a method according to example 63 or example 64,wherein the objects related to one or more primary objects include oneor more of: lane grade (related to a lane); conditional speed limit(related to a lane); and cross traffic lanes (related to anintersection).

Example 66 provides a method according to any one of the examples 63-65,wherein the objects with large geometries or no relations to otherobjects include one or more of: a routable area; a school zone driveablearea; and experimental areas for simulation purposes.

Example 67 provides a method according to any one of the examples 63-66,wherein the objects with geometries include one or more of: a driveablearea; an undriveable area; a rail line; a traffic sign; and a trafficlight.

Example 68 provides a method according to any one of the examples 63-67,wherein the objects closely related to an object with geometry includeone or more of: a buffered crosswalk area (related to a driveable area);and a driveway entry (related to a driveway driveable area).

Example 69 provides a method according to any one of the examples 63-68,wherein the relationships between objects in different shards dataentries include one or more of: lane connection (related to a startlane); and lane driveable area intersections (related to a lane).

Example 70 provides a method according to any one of the examples 62-69,further including searching for semantic objects that are located atleast partly in a section of the geographical area. The geographicalarea may be split in multiple sections. The method may further includeassigning one or more semantic objects found by the searching to theshard data entry of the shard data.

Example 71 provides a method according to example 70, wherein, after allshard data entries have been generated, a semantic object is located inonly one shard data entry.

Example 72 provides a method according to example 70 or example 71,wherein, after all shard data entries have been generated, all semanticobjects are contained in the shard data.

Example 73 provides one or more non-transitory computer-readable storagemedia including instruction for execution which, when executed by aprocessor, are operable to perform operations for handling semanticobjects for semantic map sharding. The operations may include loadingdata representative of semantic objects in a geographical area to becontained in shard data. The operations may further include loading aset of rules. The rules may include a definition of which semanticobjects are to be combined into a shard data entry of the shard data andwhich semantic objects are to be stored in separate shard data entries.

Example 74 provides one or more non-transitory computer-readable storagemedia according to example 73, wherein the semantic objects include oneor more of: primary objects; objects related to one or more primaryobjects; objects with large geometries or no relations to other objects;objects with geometries; and objects closely related to an object withgeometry. The rules may include definitions for one or more of: closelyrelated primary objects, which are to be grouped together in a singleshard; objects related to one or more primary objects, which are to bestored in the same shard data entry as the related primary object;objects with large geometries or no relations to other objects thatcannot be reasonably placed in one sections, which are to be stored inseparate shard data entries; objects with geometries, which are to beadded to a shard entry of a section that contains most of that geometry;objects closely related to an object with geometry, which are to bestored into a shard data entry together with the geometric object; andrelationships between objects in different shards data entries, whichare to be stored with one of the objects.

Example 75 provides one or more non-transitory computer-readable storagemedia according to example 74, wherein the primary objects include oneor more of: lanes; boundaries; and intersections that can be groupedtogether based on lane to boundary relationships and intersectionidentifications.

Example 76 provides one or more non-transitory computer-readable storagemedia according to example 74 or example 75, wherein the objects relatedto one or more primary objects include one or more of: lane grade(related to a lane); conditional speed limit (related to a lane); andcross traffic lanes (related to an intersection).

Example 77 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 74-76, wherein the objectswith large geometries or no relations to other objects include one ormore of: a routable area; a school zone driveable area; and experimentalareas for simulation purposes.

Example 78 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 74-77, wherein the objectswith geometries include one or more of: a driveable area; an undriveablearea; a rail line; a traffic sign; and a traffic light.

Example 79 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 74-78, wherein the objectsclosely related to an object with geometry include one or more of: abuffered crosswalk area (related to a driveable area); and a drivewayentry (related to a driveway driveable area).

Example 80 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 74-79, wherein therelationships between objects in different shards data entries includeone or more of: lane connection (related to a start lane); and lanedriveable area intersections (related to a lane).

Example 81 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 73-80, wherein the operationsfurther include generating the shard data entry data based on the datarepresentative of the semantic objects and the set of rules.

Examples of Methods for Storage of Shard Data

Example 82 provides a method for storing shard data. The method mayinclude generating and storing shard data entries. Each shard data entrymay include a definition of one or more semantic objects covered by theshard data entry. The method may further include generating and storingshard metadata. The metadata may include references to the shard dataentries. The metadata may further include, for each of the shard dataentries, data representative of a bounding box indicative of an area ina geographical area that is covered by the shard data entry.

Example 83 provides a method according to example 82, wherein the sharddata entries are stored in a database format. The shard metadata may bestored in a database format.

Example 84 provides a method according to example 82 or example 83,wherein the data representative of the bounding box is stored as fourfloating point values indicative of a minimum x-coordinate, a maximumx-coordinate, a minimum y-coordinate and a maximum y-coordinate,respectively.

Example 85 provides a method according to any one of the examples 82-84,wherein the metadata includes a subtype indicator for each of the sharddata entries. The subtype indicator may be indicative of a type ofshard.

Example 86 provides a method according to any one of the examples 82-85,wherein the subtype indicator indicates one of the following types:being related to a type of section, wherein the geographical area issplit into sections enabling semantic objects to be assigned to a shard;being related to city-wide objects, indicative of the shard includingcity-wide objects; or being related to experiments for simulationpurposes.

Example 87 provides a method according to any one of the examples 82-86,wherein the metadata includes, for each shard data entry, a string valueuniquely representing a name of the shard.

Example 88 provides a method according to any one of the examples 82-87,wherein the metadata is used to generate a shard file for each of theshard data entries. Each shard file may include a definition of thesemantic objects covered by the shard data entry.

Example 89 provides a method according to any one of the examples 82-88,wherein the metadata is used to generate a shard file for each for theshard data entries. The subtype indicator may be used as a part of afilename of the shard file.

Example 90 provides a method according to any one of the examples 82-90,further including generating and storing a shard metadata file from theshard metadata. The metadata file may include references to the shardfiles. The metadata file may further include, for each of the shardfiles, data representative of a bounding box indicative of a location ina geographical area that is covered by each of the shard files.

Example 91 provides a method according to example 90, wherein the shardmetadata file is stored in a json file format.

Example 92 provides one or more non-transitory computer-readable storagemedia including instruction for execution which, when executed by aprocessor, are operable to perform operations for storing shard data.The operations may include generating shard data entries. Each sharddata entry may include a definition of one or more semantic objectscovered by the shard data entry. The operations may further includegenerating shard metadata. The metadata may include references to theshard data entries. The metadata may further include, for each of theshard data entries, data representative of a bounding box indicative ofan area in a geographical area that is covered by the shard data entry.The operations may further include outputting the generated shard dataentries in a database format for storage in a database storage. Theoperations may further include outputting the generated shard metadatain a database format for storage in the database storage.

Example 93 provides one or more non-transitory computer-readable storagemedia according to example 92, wherein the data representative of thebounding box is output as four floating point values indicative of aminimum x-coordinate, a maximum x-coordinate, a minimum y-coordinate anda maximum y-coordinate, respectively.

Example 94 provides one or more non-transitory computer-readable storagemedia according to example 92 or example 93, wherein the metadataincludes a subtype indicator for each of the shard data entries. Thesubtype indicator may be indicative of a type of shard.

Example 95 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 92-94, wherein the subtypeindicator indicates one of the following types: being related to a typeof section, wherein the geographical area is split into sectionsenabling semantic objects to be assigned to a shard; being related tocity-wide objects, indicative of the shard including city-wide objects;or being related to experiments for simulation purposes.

Example 96 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 92-95, wherein the metadataincludes, for each shard data entry, a string value uniquelyrepresenting a name of the shard.

Example 97 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 92-96, wherein the operationsfurther include loading the shard metadata. The operations may furtherinclude, for each shard data entry, obtaining the subtype from theloaded shard metadata. The operations may further include generating ashard file for each of the shard data entries. A filename of each of theshard files may include the subtype.

Example 98 provides one or more non-transitory computer-readable storagemedia according to any one of the examples 92-97, wherein the operationsfurther include generating a shard metadata file from the shardmetadata. The metadata file may include references to the shard files.The metadata file may further include, for each of the shard files, datarepresentative of a bounding box indicative of a location in ageographical area that is covered by each of the shard files.

Example 99 provides a shard file including a definition of one or moresemantic objects covered by the shard file.

Example 100 provides a shard metadata file including references to shardfiles and, for each of the shard files, data representative of abounding box indicative of an area in a geographical area that iscovered by the shard file.

Example 101 provides a shard metadata file according to example 100,wherein the data representative of the bounding box is stored as fourfloating point values indicative of a minimum x-coordinate, a maximumx-coordinate, a minimum y-coordinate and a maximum y-coordinate,respectively.

OTHER IMPLEMENTATION NOTES, VARIATIONS, AND APPLICATIONS

According to various examples, driving behavior includes any informationrelating to how an autonomous vehicle drives. For example, drivingbehavior includes how and when the autonomous vehicle actuates itsbrakes and its accelerator, and how it steers. In particular, theautonomous vehicle is given a set of instructions (e.g., a route orplan), and the driving behavior determines how the set of instructionsis implemented to drive the car to and from various destinations, and,potentially, to stop for passengers or items. Driving behavior mayinclude a description of a controlled operation and movement of anautonomous vehicle and the manner in which the autonomous vehicleapplies traffic rules during one or more driving sessions. Drivingbehavior may additionally or alternatively include any information abouthow an autonomous vehicle calculates routes (e.g., prioritizing fastesttime vs. shortest distance), other autonomous vehicle actuation behavior(e.g., actuation of lights, windshield wipers, traction controlsettings, etc.) and/or how an autonomous vehicle responds toenvironmental stimulus (e.g., how an autonomous vehicle behaves if it israining, or if an animal jumps in front of the vehicle). Some examplesof elements that may contribute to driving behavior include accelerationconstraints, deceleration constraints, speed constraints, steeringconstraints, suspension settings, routing preferences (e.g., scenicroutes, faster routes, no highways), lighting preferences, “legalambiguity” conduct (e.g., in a solid-green left turn situation, whethera vehicle pulls out into the intersection or waits at the intersectionline), action profiles (e.g., how a vehicle turns, changes lanes, orperforms a driving maneuver), and action frequency constraints (e.g.,how often a vehicle changes lanes). Additionally, driving behaviorincludes information relating to whether the autonomous vehicle drivesand/or parks.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure, in particular aspects of a perception system for anautonomous vehicle, described herein, may be embodied in various manners(e.g., as a method, a system, a computer program product, or acomputer-readable storage medium). Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Functions described in this disclosure may beimplemented as an algorithm executed by one or more hardware processingunits, e.g., one or more microprocessors, of one or more computers. Invarious embodiments, different steps and portions of the steps of eachof the methods described herein may be performed by different processingunits. Furthermore, aspects of the present disclosure may take the formof a computer program product embodied in one or more computer readablemedium(s), preferably non-transitory, having computer readable programcode embodied, e.g., stored, thereon. In various embodiments, such acomputer program may, for example, be downloaded (updated) to theexisting devices and systems (e.g., to the existing perception systemdevices and/or their controllers, etc.) or be stored upon manufacturingof these devices and systems.

The preceding disclosure presents various descriptions of specificcertain embodiments. However, the innovations described herein can beembodied in a multitude of different ways, for example, as defined andcovered by the claims and/or select examples. In the followingdescription, reference is made to the drawings, where like referencenumerals can indicate identical or functionally similar elements. Itwill be understood that elements illustrated in the drawings are notnecessarily drawn to scale. Moreover, it will be understood that certainembodiments can include more elements than illustrated in a drawingand/or a subset of the elements illustrated in a drawing. Further, someembodiments can incorporate any suitable combination of features fromtwo or more drawings.

The preceding disclosure describes various illustrative embodiments andexamples for implementing the features and functionality of the presentdisclosure. While particular components, arrangements, and/or featuresare described below in connection with various example embodiments,these are merely examples used to simplify the present disclosure andare not intended to be limiting. It will of course be appreciated thatin the development of any actual embodiment, numerousimplementation-specific decisions must be made to achieve thedeveloper's specific goals, including compliance with system, business,and/or legal constraints, which may vary from one implementation toanother. Moreover, it will be appreciated that, while such a developmenteffort might be complex and time-consuming; it would nevertheless be aroutine undertaking for those of ordinary skill in the art having thebenefit of this disclosure.

In the present disclosure, reference may be made to the spatialrelationships between various components and to the spatial orientationof various aspects of components as depicted in the attached drawings.However, as will be recognized by those skilled in the art after acomplete reading of the present disclosure, the devices, components,members, apparatuses, etc. described herein may be positioned in anydesired orientation. Thus, the use of terms such as “above”, “below”,“upper”, “lower”, “top”, “bottom”, or other similar terms to describe aspatial relationship between various components or to describe thespatial orientation of aspects of such components, should be understoodto describe a relative relationship between the components or a spatialorientation of aspects of such components, respectively, as thecomponents described herein may be oriented in any desired direction.When used to describe a range of dimensions or other characteristics(e.g., time, pressure, temperature, length, width, etc.) of an element,operations, and/or conditions, the phrase “between X and Y” represents arange that includes X and Y. If used, the terms “substantially,”“approximately,” “about,” etc., may be used to generally refer to beingwithin +/−20% of a target value, e.g., within +/−10% of a target value,based on the context of a particular value as described herein or asknown in the art. For the purposes of the present disclosure, the phrase“A and/or B” or notation “A/B” means (A), (B), or (A and B). For thepurposes of the present disclosure, the phrase “A, B, and/or C” ornotation “A/B/C” mean (A), (B), (C), (A and B), (A and C), (B and C), or(A, B, and C).

Other features and advantages of the disclosure will be apparent fromthe description and the claims. Note that all optional features of theapparatus described above may also be implemented with respect to themethod or process described herein and specifics in the examples may beused anywhere in one or more embodiments.

The ‘means for’ in these instances (above) can include (but is notlimited to) using any suitable component discussed herein, along withany suitable software, circuitry, hub, computer code, logic, algorithms,hardware, controller, interface, link, bus, communication pathway, etc.In a second example, the system includes memory that further includesmachine-readable instructions that when executed cause the system toperform any of the activities discussed above.

1. A method for storing shard data, the method comprising: utilizingguiding sections defining different parts of a geographical area toidentify semantic objects within each guiding section; applying one ormore map consistency rules to determine whether semantic objects withina given guiding section are to be kept together in a shard; generatingand storing shard data entries in a shard-to-object relations database,wherein each shard data entry in the shard-to-object relations databaseidentifies a given shard and which semantic objects are part of thegiven shard; and generating and storing shard metadata entries, whereineach shard metadata entry corresponds to a given shard, and includes:(1) an integer value as an identifier of the given shard; (2) anon-unique string value representing a subtype of the given shard; (3) aunique string value representing a name of the given shard constructedfrom the non-unique string value and a sequence number; and (4) fourfloating point values defining corners of a bounding box covered by thesemantic objects of the given shard; exporting shard data and storingthe shard data as shard files based on the shard metadata entries andthe shard-to-object relations database; and providing shard files toclients including one or more of an autonomous vehicle, a vehicle, and acomputer environment.
 2. The method according to claim 1, wherein theshard data entries are stored in a database format, and wherein theshard metadata entries are stored in a database format.
 3. The methodaccording to claim 1, wherein the four floating point values areindicative of a minimum x-coordinate, a maximum x-coordinate, a minimumy-coordinate and a maximum y-coordinate, respectively.
 4. The methodaccording to claim 1, wherein the subtype indicates the shard beingrelated to a type of the guiding section used in generating the shard.5. The method according to claim 1, wherein the subtype indicates theshard being related to city-wide objects, indicative of the shardincluding city-wide objects.
 6. The method according to claim 1, whereinthe subtype indicates the shard being related to experiments forsimulation purposes
 7. The method according to claim 1, wherein theshard metadata entry for the given shard used to generate the shard filefor each of the shard data entries, wherein each shard file comprises adefinition of the semantic objects covered by the shard data entry. 8.The method according to claim 1, wherein the shard metadata entry forthe given shard is used to generate the shard file for each for theshard data entries, and wherein the subtype is used as a part of afilename of the shard file.
 9. The method according to claim 1, furthercomprising: generating and storing a shard metadata file from the shardmetadata entries, wherein the shard metadata file comprises referencesto the shard file.
 10. The method according to claim 9, wherein theshard metadata file is stored in a json file format.
 11. One or morenon-transitory computer-readable storage media comprising instructionfor execution which, when executed by a processor, are operable toperform operations for storing shard data, the operations comprising:utilizing guiding sections defining different arts of a geographicalarea to identify semantic objects within each guiding section; applyingone or more map consistency rules to determine whether semantic objectswithin a given guiding section are to be kept to ether in a shard;generating shard data entries in a shard-to-object relations database,wherein each shard data entry in the shard-to-object relations databaseidentifies a given shard and which semantic objects are part of a givenshard; generating shard metadata entries, wherein each shard metadataentry corresponds to a given shard, and includes: (1) an integer valueas an identifier of the given shard; (2) a non-unique string valuerepresenting a subtype of the given shard; (3) a unique string valuerepresenting a name of the given shard constructed from the non-uniquestring value and a sequence number; and (4) four floating point valuesdefining corners of a bounding box covered by the semantic objects ofthe given shard; exporting shard data and storing the shard data asshard files based on the shard metadata entries and the shard-to-objectrelations database; and providing shard files to clients including oneor more of an autonomous vehicle a vehicle, and a computer environment.12. The one or more non-transitory computer-readable storage mediaaccording to claim 11, wherein the four floating point values areindicative of a minimum x-coordinate, a maximum x-coordinate, a minimumy-coordinate and a maximum y-coordinate, respectively.
 13. The one ormore non-transitory computer-readable storage media according to claim11, wherein the subtype indicates the shard being related to a type ofthe guiding section used in generating the shard.
 14. The one or morenon-transitory computer-readable storage media according to claim 11,wherein the subtype indicates the shard being related to city-wideobjects, indicative of the shard including city-wide objects.
 15. Theone or more non-transitory computer-readable storage media according toclaim 11, wherein the subtype indicates the shard being related toexperiments for simulation purposes.
 16. The one or more non-transitorycomputer-readable storage media according to claim 11, wherein theoperations further comprise: loading the shard metadata entries; foreach shard data entry, obtaining the subtype from the loaded shardmetadata entries; and generating the shard file for each of the sharddata entries, wherein a filename of each of the shard files comprisesthe subtype.
 17. The one or more non-transitory computer-readablestorage media according to claim 11, wherein the operations furthercomprise: generating a shard metadata file from the shard metadataentries, wherein the shard metadata file comprises references to theshard files.
 18. A computer system comprising: one or more processors;one or more non-transient computer-readable storage devices storinginstructions; one or more database storage devices storing one or moredatabases; a database exporter, encoded in the instructions to exportshard data from the one or more database storage devices based on: ashard-to-object relations database stored on the one or more databasestorage devices, wherein each entry in the shard-to-object relationsdatabase identifies a given shard and which semantic objects are part ofthe given shard; and shard metadata entries stored on the one or moredatabase storage devices, wherein each metadata entry corresponds to agiven shard, and includes: (1) an integer value as an identifier of thegiven shard; (2) a non-unique string value representing a subtype of thegiven shard; (3) a unique string value representing a name of the givenshard constructed from the non-unique string value and a sequencenumber; and (4) four floating point values defining corners of abounding box covered by the semantic objects of the given shard; a filestorage system to receive shard data exported b the database exporterand to store the shard data as shard files, where one shard fileincludes data for one shard; and an uploader, encoded in theinstructions, to upload the shard files to clients including one or moreof an autonomous vehicle, a vehicle, and a computer environment.
 19. Thecomputer system of claim 18, wherein a filename of the shard filecomprises the subtype.
 20. The computer system of claim 18, wherein thefour floating point values indicative of a minimum x-coordinate, amaximum x-coordinate, a minimum y-coordinate and a maximum y-coordinate,respectively.